Thursday, September 22, 2011


: The concept of reliability originates from Spearman’s early work with factor analysis and measurement errors over hundred years ago. However, the importance of the reliability of measurement scales has been partially obscured because of poor estimators, such as Cronbach’s alpha is widely applied despite the poor estimate of the measurement error variance used. Subsequently Cronbach’s alpha underestimates the reliability and may even give absurd, negative estimates, it remains to be the most widely applied estimator of reliability for reason of easiness; a quick method for practical needs—long before the era of computers. [Vehkalahti,Puntanen &Tarkkonen, 2006]The availability of hi-speed computing technology, easily available in our lap top will change such necessity in the name of accuracy and precision. Cronbach Alpha always exceeds the maximum reliability possible for the measures underlying for a given dataset. This misleads the test-user into believing a test has better measurement characteristics than it actually has. It overstates the reliability of the test-independent, generalizable measures the test is intended to imply. For inference beyond the test, Rasch reliability is more conservative and less misleading. [Linacre, 1997]
Nunally (1978) definition of Cronbach-alpha has been grossly misused just like Krejcie & Morgan (1970) for 'random' sampling size; but many keep on citing it NOT knowing the precision is NOT in place as compared to other more current methods as discussed above. Cohen (1992) Statistical.Power Analysis shows an alternative method of sampling size for smaller size depending on d stats. test to be employed; correlation or multiple regression. (see: .
Linacre (1994) employs JMLE to determine d sample size and meet sufficient statistics principles. see: fundamentals has to be observed n CANNOT simply be breached for convenience of a given case. Thus, Rasch reliability, MNSQ, z-std, PMC, eigenvalue ratio, item indepencence etc. are stats. values dat must be in place before an item is considered a fit.

RASCH : Smaller sample size with lesser error..

In determining a practical sample size, one have to understand the method of analysis to be employed. In general practice, sample size by random sampling is done using the table developed by Krejcie &Morgan, (1970) Determining sample size for research activities. J.Educational and Psychological Measurement, 30, 607-610. 

According to Salant and Dillman (1994), the size of the sample is determined by four factors: (1) how much sampling error can be tolerated; (2)population size; (3) how varied the population is with respect to the characteristics of interest; and (4) the smallest subgroup within the sample for which estimates are needed. One of the common reference is Cohen Statistical Power Analysis (1992) being one of the most popular approaches in the behavioural sciences in calculating the required sampling size. In Krejcie and Morgan (1970), the estimated random sampling size for a population of 500 is 217. However, the estimated sampling size calculated using Cohen (1992) differs according to the type of statistical tests employed by the researcher. The sample size that is required for a correlational study is 85 while a multiple regression analysis requires 116.

Rasch statistical analysis offers a better mathematics with even smaller sample size but of sufficient stability. see, "Sample Size and Item Calibration Stability. Linacre JM. Rasch Measurement Transactions 1994 7:4 p.328"Rasch analysis can handsomely handle a sample size of 25-30 to generate a sound 95%CL statistics and 50-60 for a 99% CL.

Another achievement...

Rasch application in Malaysian education scenario has made another achievement at the international arena. Pn.Nazlinda Abdullah from the Faculty of Education, UiTM was awarded the Best Student Paper in recent PROMS 2011 in Singapore. see: